Text correction processing

ABSTRACT

Text correction processing is disclosed. An initial score is assigned to each of a plurality of candidate sequences of one or more characters, based at least in part on a keyboard geometry-based value associated with the received user input with respect to the candidate key. Further processing is performed with respect to a subset of the candidate sequences having the highest initial score(s) to determine for each candidate sequence in the subset a refined score. A candidate sequence is selected for inclusion in a result set based at least in part on a determination that a refined score of the selected candidate is higher than an initial score of one or more candidate sequences that are not included in the subset and with respect to which the further processing has not been performed.

BACKGROUND OF THE INVENTION

User interfaces are provided to enable users to enter text or othercontent elements into application documents, such as a word processingdocument. In touch-interface smart phones and other touch interfacedevices, for example, a traditional “QWERTY” or other keyboard may bedisplayed via a touch-sensitive display, such as a capacitive display.User touches are processed initially to determine which key was mostlikely intended. The key to which a touch is mapped may then bedisplayed, for example in a text entry field in which the user isentering text, a document or other application object, etc.

In addition to mapping touches to keys, sequences of touches must bemapped to words and in some system auto-correction and/orauto-completion suggestions are identified, evaluated, and ifappropriate suggested to the user as inline corrections/completions.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a flow chart illustrating an embodiment of a process toreceive user input and display associated text.

FIG. 2A is a block diagram illustrating an embodiment of a systemconfigured to receive and process user input.

FIG. 2B is a block diagram illustrating an embodiment of a languagemodel.

FIG. 3 is a flow diagram illustrating an embodiment of a process toreceive and process user input.

FIG. 4 is a flow diagram illustrating an embodiment of a process toreceive and process user input.

FIG. 5 is a flow diagram illustrating an embodiment of a process todetermine an extended set of candidate sequence search nodes.

FIG. 6 is a diagram illustrating an example of forming and computinginitial probability based scores for a set of extended candidatesequences in various embodiments.

FIG. 7 is a flow diagram illustrating an embodiment of a process to mapuser inputs to keys.

FIG. 8A is a block diagram illustrating an embodiment of a systemconfigured to perform unigram analysis.

FIG. 8B is a block diagram illustrating an embodiment of a systemconfigured to extend unigram analysis to detect “space” key entryerrors.

FIG. 9 is a flow diagram illustrating an embodiment of a process todetect “space” key entry errors.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

Processing of user input, such as user touches on a keyboard interfacedisplayed in a touch-sensitive display device, is disclosed. As touchesto the interface are received, each touch is mapped to one or morecandidate keys associated with the touch, for example, one or more keysdisplayed nearest the touch. Each touch is mapped to a key selected tobe displayed in the document or other text entry area being displayed tothe user. In addition, sequences of touches are evaluated to determineauto-correction and/or auto-completion suggestions, if any, to bedisplayed to the user as “inline” corrections, shown for example in abubble or otherwise adjacent and/or near the sequence currentlydisplayed, i.e., to sequence of keys to which a current sequence oftouches have been mapped. In various embodiments, keyboard geometryand/or language models are used to map touches to keys and/or togenerate auto-correction and/or auto-completion candidates.

In some embodiments, a set of candidate key sequences are updated assubsequent touches are received. An initial score is assigned to each ofa plurality of candidate sequences each of which incorporates acandidate key that has been identified as a candidate with which a touchor other received user input is associated. In some embodiments, theinitial score is based at least in part on a keyboard geometry-basedvalue associated with the received user input with respect to thecandidate key, for example, an error vector from a touch to the locationat which the key was displayed. At least initially, only a subset ofcandidate sequences so derived are further evaluated to determine foreach candidate sequence in the subset a refined score. The initial scoreof each candidate sequence is configured to comprise an upper bound of arefined score for that sequence, such that if the refined score of acandidate sequence in the subset is higher than the initial score of oneor more candidate sequences not in the subset, the candidate sequencewith the refined score may be included in a starting set of candidatesfor a next iteration (for example, processing a subsequent touch)without first determining a refined score for such candidate sequencesnot in the subset. In this way, further processing to determine refinedscores for candidates not in the subset may be delayed and possiblyavoided, for example if the starting set is filled with a prescribednumber of members before such further processing is performed.

In some embodiments, unigram (e.g., one word at a time) analysis isextended to detect and correct automatically errors by which a user whointended to select a “space” key to insert a space between words entersa touch or other input that instead gets mapped at least initially toanother key, such as a key adjacent to the space key as displayed. Invarious embodiments, a unigram model provides the probability of a givenkey (character) given a preceding sequence of zero or more characters.In some embodiments, a candidate sequence is evaluated based at least inpart on the respective probability of each key in the candidate sequenceoccurring after the sequence that precedes that key in the sequence. Insome embodiments, the probabilities are expressed as values between zeroand one and the respective probabilities are multiplied together todetermine a probability for the sequence. In various embodiments, theunigram approach is extended to include the possibility that a “space”key, as opposed to a letter, was intended to be entered. In someembodiments, the probability associated with the space key is equal toand/or determined at least in part based on a probability of a keyimmediately following the space in the candidate sequence occurring atthe beginning of a word.

FIG. 1 is a flow chart illustrating an embodiment of a process toreceive user input and display associated text. In the example shown, auser input, such as a user touch on a touch-sensitive display or userinput provided via another user input device, is received (102). Theinput (i.e., touch) is mapped to a corresponding character to bedisplayed at least initially (104). The sequence of touches and/or keys(characters) to which touches have (at least initially or tentatively)been mapped is evaluated to identify an auto-correction and/orauto-completion suggestion, if any, to be provided (for example,displayed adjacent to the sequence of characters that is currentlydisplayed based on the sequence of touches) (106). The process repeatswith each subsequent touch or other input, if any, until done (108).

FIG. 2A is a block diagram illustrating an embodiment of a systemconfigured to receive and process user input. In the example shown, thesystem 200 includes a user input interface 202, an auto-correctionengine 204, and a display component 206. Examples of the system 200include, without limitation, a personal, laptop, tablet, or otherportable computer, a smart phone, personal data assistant device, etc.In some embodiments, user input interface 202 comprises a processingmodule, such as software running on a processor (not shown) comprisingsystem 200, configured to receive and process user “touches” or otherinputs entered via a “soft” keyboard or other interface displayed on atouch-sensitive display device associated with display component 206. Astouches (or other inputs) are received, user input interface 202provides associated data, such as touch coordinates, to auto-correctionengine 204. Auto-correction engine 204 in various embodiments comprisesa functional module provided by software executing on a processorcomprising system 200. Auto-correction engine 204 is configured, invarious embodiments, to use one or more of a geometry model 208 and alanguage model 212 to determine based on a received sequence of one ormore touches a corresponding sequence of one or more characters todisplay via a display device associated with display component 206. Invarious embodiments, auto-correction engine 204 is configured to use oneor more of a geometry model 208 and a language model 212 to determinebased on a received sequence of one or more touches and/or acorresponding sequence of one or more characters to which the sequenceof touches has been mapped an auto-correction and/or auto-completionsuggestion to be provided, for example by displaying the suggestion as“inline” or other adjacent text via a display device associated withdisplay component 206. In various embodiments, auto-correction engine204 is configured to implement “lazy” evaluation of candidate keysequences and/or to extend unigram model processing for received textinput to include candidate sequences that include a “space” key, asdisclosed herein.

FIG. 2B is a block diagram illustrating an embodiment of a languagemodel. In the example shown, language model 212 of FIG. 2A includes aunigram dictionary 240 and a class (part-of-speech) trigram model 242.Unigram dictionary provides for a given candidate key to which to map aninput, given one or more prior keys in the sequence, a probability thatthe candidate key would have been intended. Class (POS) trigram model242 in various embodiments provides probabilities that a candidate wordwas intended to be entered given the preceding two words and therespective parts of speech of the three words.

“Lazy” Evaluation of Candidate Key Sequences

FIG. 3 is a flow diagram illustrating an embodiment of a process toreceive and process user input. In the example shown, when a “touch” orother input is received (302), keyboard (or other interface)geometry-based error vectors are generated, probabilities and/or otherscores derived therefrom are determined, and if more than one key is acandidate to be mapped to the touch the candidates are sorted by theirrespective scores (304). For example, based on the x-y coordinates of areceived touch relative to the respective positions at which the softkeys “E” and “R” are displayed, such as the respective linear distancefrom the touch to each respective candidate key, a probability P_(E) maybe associated with the candidate key “E” and a probability P_(R)associated with the candidate key “R”. Language, geometry, and othermodels are used in this example to refine the score of a key candidatecurrently having the highest (or next highest, in any iterationsubsequent to the first) score among the candidates (306). At leastinitially, in various embodiments the score(s) of one or more othercandidates is/are not refined. Examples of using a language model tocompute a refined score include, without limitation, adjusting a scoreassociated with a candidate key based at least in part on a languagemodel-determined probability of a string that would result from thetouch being mapped to that candidate key, and/or a word comprising thatstring, occurring in a document or other text in a language with whichthe text input is associated. For example the probability of a touchbeing mapped to the key “R” may be reduced if preceding touches havebeen mapped to the keys T-H-E-R and based on a language model it isdetermined that the sequence T-H-E-R-R has a relatively low probabilityof occurring; whereas a language-model refined score for a candidate key“E” in the same context may be determined to have a relatively higherprobability, based on a higher likelihood of the sequence T-H-E-R-Eoccurring.

In some embodiments, initial scores are scaled to fall within a rangefrom zero to one. The respective initial score for each candidate key isconsidered to comprise an upper bound score for that key. Refined scoresare determined by multiplying the initial score by further probabilitiesthat likewise have been scaled to values between zero and one, such asprobabilities determined by considering information other than the touchor other input currently being evaluated, such as preceding touchesand/or words entered and/or determined to have been entered by the user,including without limitation language model-derived probabilities asdescribed above. As a result of such an approach, if the refine score ofcandidate A is greater than the initial, unrefined score of candidate B,it can be concluded that the refined score of candidate A will (orwould) be greater than the refined score of candidate B, if it werecomputed, since the refine score of candidate B would always be equal toor less than the initial score of candidate B. In various embodiments,this observation and approach is used to perform further processing ofkey candidates and/or associated candidate key sequences only “lazily”,and to avoid performing such further processing with respect tocandidates that can with confidence be excluded without performing suchfurther processing to determine for such candidates a refined score.

In the example shown in FIG. 3, for example, if the refined scoredetermined for the key candidate currently being considered is greaterthan the next highest score in the set of candidates, if any (308), thetouch is mapped to the candidate key with which the refined score isassociated (310). Otherwise, processing proceeds to consideration of thecandidate key that has the highest score (i.e., now that the scorerefined at 306 has been refined, in this case to a score lower than thescore that is now highest) (314). The initial and currently highestscore is then refined (306) and compared to other scores in the set(308), and if the refined score is the highest the touch is mapped tothe current key (310), otherwise processing continues based on thecurrently highest score among the candidates until a refined score thatis higher than the score of any other key candidate is found, afterwhich the process ends (312).

FIG. 4 is a flow diagram illustrating an embodiment of a process toreceive and process user input. In the example shown, when a touch y_(n)or other input is received (402), for each candidate sequence currentlyin a best N set of candidates, each resulting sequence obtained byextending the candidate sequence by each candidate key x_(n) that hasbeen identified as a candidate key to which the touch y_(n) may bemapped, based on an error vector and/or other geometry based informationfor example, is evaluated for inclusion in an updated best N set ofcandidates (404). For example, if N=3 and two possibilities have beenidentified for a next touch y_(n), then six possible sequences areevaluated, starting with a candidate sequence having a highest scoreamong the candidates in the best N set of candidate sequences from theprevious iteration, after such scores have been updated to reflectprobabilities associated with the respective key candidates (406). Ifthe score is a refined score (note that in this example no score wouldbe refined in the very first iteration) (408), then the candidatesequence currently being considered is added to the updated best N set(412). If the high score is not a refined score, further processing,such as language model based processing, is performed to refine thescore (410), after which the candidate sequence having the highest score(after refinement of the score just refined) is evaluated (406). As inthe above-described iteration, if the high score is refined score (408),for example because the score just refined remains higher than any otherscore in the set of extended candidate sequences, then the candidatesequence with which the refined score is associated is added to the set(412), and so on. Processing continues until N candidate sequences havebeen added to the new/updated best N set of candidate sequences (414),after which the process of FIG. 4 ends. In this way, scores are refinedin successive iterations as and only to an extent required to identifythe best (most probable) N candidate sequences to be carried forward forconsideration based on a next touch, if any. Candidate sequences whoseinitial, unrefined scores are lower than the refined score of N othercandidate sequences, if any, are excluded without further processingever being performed to refine their respective scores.

FIG. 5 is a flow diagram illustrating an embodiment of a process todetermine an extended set of candidate sequence search nodes. In someembodiments, the process of FIG. 5 is used to implement 404 of FIG. 4.In the example shown, a set of extended candidate sequences is formed byappending to each member of a previous (i.e., current, not yet updated)set of best N candidate sequences each candidate key x, associated witha touch y_(n) (502). For each candidate sequence in the resulting set ofextended candidate sequences, an initial updated probability (and/orother score) is computed (504) based at least in part on (1) aprobability associated with a candidate sequence that was extended toform the extended candidate sequence, for example a probability (such asa refined score as described above) determined for the candidatesequence in a prior iteration based on a preceding touch, and (2) aprobability associated with the candidate key x_(n), for example anerror vector or other value indicative of the likelihood that thecandidate key x_(n) was intended by the touch y_(n). In someembodiments, the previously computed probability or other score ismultiplied by the error vector-based probability or other scoreassociated with the candidate key x_(n) to determine the initialprobability for the extended candidate sequence.

FIG. 6 is a diagram illustrating an example of forming and computinginitial probability based scores for a set of extended candidatesequences in various embodiments. In the example shown, a current set602 comprising the three best candidate sequences determined in a prioriteration is shown. The set includes three candidate sequences (T-H,T-R, and Y-H), each have a corresponding score shown to the right of thesequence. In the example shown, a subsequent touch has been associatedwith two key candidates, a first candidate “E” having an initial scoreof 0.6 associated with it, and a second candidate “R” having an initialscore of 0.4 associated with it. In some embodiments, the scores forcandidate keys are based at least in part on geometry, such as errorvectors. Arrow 604 indicates the process by which each candidatesequence in set 602 is extended by each of the two candidate keys toyield the resulting extended set 606. For each extended sequence in set606, a corresponding score is computed, in this example by multiplyingthe score shown in set 602 for the two-character sequence extended toform the extended sequence in 606 by the score associated with thecandidate key used to extend the sequence. For example, the sequenceT-H-E is shown as having an initial score of 0.42 computed bymultiplying the score associated previously with sequence T-H (0.70) bythe score associated with candidate key E (0.60), i.e., 0.70×0.60=0.42.

In various embodiments, the respective initial scores shown in FIG. 6for extended sequences are evaluated lazily, for example as describedabove in connection with FIG. 4, to populate a new/update set of thebest three candidates from the set 606 of extended sequences. Forexample, as shown in FIG. 6 the sequence T-H-E has the highest initialscore. In some embodiments, further processing would be performed firstwith respect to the sequence 0.42. If the resulting refined scoreremained higher than any other score in the set, refined or not, thenthe sequence T-H-E would be added to the new “best three” set. Ifinstead another, unrefined score were higher, that other score would berefined and then checked to see if it remained the highest. Successiveiterations would be performed until three (in this example) sequenceshad been added to the new/update set of best candidate sequences tocarry forward to use in processing the next touch.

In some embodiments, once one or more auto-correction and/orauto-completion candidate words have been determined, further processingis performed to refine auto-correction candidate scores based oncontextual information, such as one or more words preceding a currentsequence being evaluated. For example, as words are identified as havingbeen entered by the user in some embodiments part-of-speech taggingand/or other language model based processing is performed to generatecontextual information that is used to evaluate one or moreauto-correction and/or completion candidates associated with a sequencecurrently being evaluated. For example, if a definite article followedby a noun has been typed and tagged, in some embodiments anauto-correction candidate that is a verb may be considered more likelythan a second candidate that is not a verb. The term “n-gram” is used torefer to text processing in which decisions are made based at least inpart on a context comprising a set of n words that include and/orotherwise provide context for the text being processed.

FIG. 7 is a flow diagram illustrating an embodiment of a process to mapuser inputs to keys. In the example shown, auto-correction candidatesassociated with a sequence of touches or other inputs, along with a setof preceding and words comprising an n-gram context for the sequencecurrently being evaluated, are received (702). Language model-basedtechniques are used to evaluated the key candidates at least in part byconsidering the n-gram context information (704), for example asdescribed above.

Extending Unigram Analysis to Incorporate the “Space” Key

In various embodiments, unigram analysis is extended to incorporateconsideration of the “space” bar or other key, for example, in order todetect and suggest auto-correction of errors by which a user whointended to enter a space instead made an input (e.g., soft keyboardtouch) that was mapped to a key adjacent to the space key, such as a“v”, “b”, or “n” in a keyboard using the familiar QWERTY layout.

FIG. 8A is a block diagram illustrating an embodiment of a systemconfigured to perform unigram analysis. In the example shown, a treerepresentation 800 of a unigram model is shown. The model provides foreach candidate keys in a candidate sequence an incremental probabilityof that key occurring after the key preceding it in the candidatesequence. In the example shown, for example, the model would provide fora candidate sequence G-O-N a first probability of the character “G”occurring at the beginning of a word, a second probability of an “O”occurring after a “G” (represented by arrow 802), and a thirdprobability of an “N” occurring after an “O” (represented by arrow 804).The three probabilities would be used, in various implementations, todetermine and/or adjust a probability or other score for the candidatesequence G-O-N, for example, by multiplying the first, second, and thirdprobabilities together.

In prior approaches, a unigram model and analysis typically would beused to evaluate a sequence believed to comprise a single word orportion thereof. For example, on detection of the end of a word, forexample detecting entry of a space and/or end of sentence punctuation,in prior approaches a unigram model such as the one represented in FIG.8A might be used to identify and/or evaluate candidates forauto-correction. In the example shown in FIG. 8A, for example, thedetected sequence G-O-N-H might be determined based on the model to besignificantly less likely to have been intended than the sequenceG-O-N-E, at least in part due to the relatively lower probability (see806) of an “H” occurring after an “N”, rather than an “E” (808).

Extending a unigram model and analysis to detect errors resulting in akey other than the “space” key being mapped to a touch or other userinput when the user in fact intended to type a space is disclosed.

FIG. 8B is a block diagram illustrating an embodiment of a systemconfigured to extend unigram analysis to detect “space” key entryerrors. In the example shown, a unigram model such as the one shown inFIG. 8A has been extended to include a transition 820 back to the toplevel of the model. In some embodiments, at the top level of the modelthe probability of a character occurring at the start of a word areprovided for each character (e.g., letter), for example 822, 824. Insome embodiments, a candidate sequence of keys may include a “space”key. The candidate sequence including the “space” is processed as aunigram. In various embodiments, the likelihood that a particular touchwill be mapped to the “space” key, as opposed to an adjacent key(result, for example, in a space being displayed to the user as havingbeen typed) is determined at least in part by associating with the“space” key in the unigram analysis a probability associated with atransition such as transition 820 back to the top level of the unigrammodel. In some embodiments, the probability associated with a transitionback to the top level, such as 820, is determined to be the same as aprobability of a next key following the space key in the candidatesequence occurring at the beginning of a word (e.g., 822, 824). Forexample, in evaluating the candidate sequences “GONHOME” and “GO HOME”,the likelihood that the first sequence was intended would be determinedin part by the probability 822 of “G” occurring at the start of a wordand the respective probabilities associated with the transitions 826,828, and 830, respectively; while the likelihood of the third touchinstead being mapped to a “space” would be determined in variousembodiments by the probability 822 of a “G” occurring at the start of aword, and the probabilities associated with the transitions 826 and 820.In some embodiments, as noted above, the probability associated withtransition 820 would be determined in this example at least in partbased on the likelihood 824 of a character (here “H”) following the“space” in the candidate sequence occurring at the start of a word.

FIG. 9 is a flow diagram illustrating an embodiment of a process todetect “space” key entry errors. In the example shown, when a touch isreceived (902) candidate key sequences are updated and evaluated (904).If a candidate sequence includes a “space” key (906), a probability isassigned to the “space” key as a candidate at least in part by waitingfor a subsequent touch y_(n+1) to be mapped to an associated key x_(n+1)and associating with the “space” key as a candidate key x_(n) to whichto map an associated touch y_(n) a probability associated with thefollowing candidate key x_(n+1) occurring at the beginning of a word(908). Processing continues until done (910), for example it isdetermined that a complete and correct word has been entered anddisplayed.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is: 1-25. (canceled)
 26. A computer-implemented methodof processing user input, comprising: mapping a sequence of touches onan input device to candidate sequences of one or more characters of adisplayed keyboard interface; assigning to each of the candidatesequences an initial score based at least in part on a keyboardgeometry-based value associated with each of the touches with respect tocandidate keys; determining a refined score with respect to a subset ofthe candidate sequences having the highest initial scores; and selectinga candidate sequence for inclusion in a result set if the refined scoreis greater than an initial score of a candidate sequence not included inthe subset of the candidate sequences having the highest initial scores.27. The method of claim 26, wherein the sequence of touches comprisestouches on a touch-sensitive display.
 28. The method of claim 26,wherein the keyboard geometry-based value comprises an error vectorrepresenting a detected location of user input relative to a location atwhich a user interface image associated with the candidate key isdisplayed.
 29. The method of claim 26, further comprising using alanguage model to determine the refined score.
 30. The method of claim29, wherein the language model is used to determine a probability ofoccurrence of the candidate sequence in a language with which the userinput is associated.
 31. The method of claim 26, wherein result setcomprises a set of N candidate sequences to be carried forward into anext iteration of processing.
 32. The method of claim 31, wherein theresult set comprises a starting set of candidate sequences to be used toevaluate a next user input.
 33. The method of claim 31, wherein thesteps of determining and selecting are repeated until N candidatesequences have been added to the result set.
 34. The method of claim 33,wherein an iteration of the method of claim 1 ends without furtherprocessing being performed on any further candidate sequences in theplurality of candidate sequences once N candidate sequences have beenadded to the result set.
 35. The method of claim 26, wherein thedetermining includes using a language model and an n-gram context dataassociated with the candidate sequence to determine the refined score.36. The method of claim 35, wherein the n-gram context data comprisestwo words preceding the candidate sequence.
 37. The method of claim 26,wherein the determining includes using a language model and an n-gramcontext data associated with the candidate sequence to determine atleast in part whether the received user input should be mapped to thecandidate key.
 38. A system configured to process text input,comprising: an input device configured to receive a sequence of userinputs; and a processor coupled to the input device and configured to:map a sequence of touches on the input device to candidate sequences ofone or more characters of a displayed keyboard interface; assign to eachof the candidate sequences an initial score based at least in part on akeyboard geometry-based value associated with each of the touches withrespect to candidate keys; determine a refined score with respect to asubset of the candidate sequences having the highest initial scores; andselect a candidate sequence for inclusion in a result set if the refinedscore is greater than an initial score of a candidate sequence notincluded in the subset of the candidate sequences having the highestinitial scores.
 39. The system of claim 38, wherein the input devicecomprises a touch-sensitive device.
 40. The system of claim 38, whereinthe input device comprises a touch-sensitive display device and theprocessor is configured to display on the display device, for eachreceived user input, a selected character to which the input has beenmapped.
 41. The system of claim 38, wherein the processor is furtherconfigured to generate, based at least in part on the result set, anauto-correction/completion candidate.
 42. The system of claim 41,wherein the processor is further configured to display a selectedauto-correction/completion candidate.
 43. The system of claim 38,wherein the determining includes using a language model to determine therefined score; and wherein the system further includes a storage deviceconfigured to store the language model.
 44. A non-transitory computerprogram product for processing user input, the computer program productbeing embodied in a computer readable storage medium and comprisingcomputer instructions for: mapping a sequence of touches on an inputdevice to candidate sequences of one or more characters of a displayedkeyboard interface; assigning to each of the candidate sequences aninitial score based at least in part on a keyboard geometry-based valueassociated with each of the touches with respect to candidate keys;determining a refined score with respect to a subset of the candidatesequences having the highest initial scores; and selecting a candidatesequence for inclusion in a result set if the refined score is greaterthan an initial score of a candidate sequence not included in the subsetof the candidate sequences having the highest initial scores.
 45. Thenon-transitory computer program product of claim 44, wherein thesequence of touches comprises touches on a touch-sensitive display. 46.The non-transitory computer program product of claim 44, wherein thekeyboard geometry-based value comprises an error vector representing adetected location of user input relative to a location at which a userinterface image associated with the candidate key is displayed.
 47. Thenon-transitory computer program product of claim 44, further comprisingcomputer instructions for using a language model to determine therefined score.
 48. The non-transitory computer program product of claim47, wherein the language model is used to determine a probability ofoccurrence of the candidate sequence in a language with which the userinput is associated.
 49. The non-transitory computer program product ofclaim 38, wherein result set comprises a set of N candidate sequences tobe carried forward into a next iteration of processing.
 50. Thenon-transitory computer program product of claim 49, wherein the resultset comprises a starting set of candidate sequences to be used toevaluate a next user input.
 51. The non-transitory computer programproduct of claim 49, wherein the steps of determining and selecting arerepeated until N candidate sequences have been added to the result set.